10 Best Speech-to-Text APIs for Real-Time Transcription

As businesses rely more on digital transformation to improve customer satisfaction, the ability to convert speech to text (STT) has become imperative for a range of applications. In fact, customer service is the prominent factor, pushing for 81% of STT adoption. The advance of Artificial Intelligence workflows has made it even more important for companies to steer clear of competition. 

It is all for the right reasons: real-time transcription enables smoother workflows, and captures conversations, meetings, and other interactions with remarkable ease. 

Consequently, the demand for a speech-to-text model that provides accurate, fast, and reliable transcriptions has grown across sectors like healthcare, legal, media, and beyond. This blog will explore the top 10 best speech-to-text APIs available today, highlighting their pros, cons, specific use cases and much more. This analysis serves every business looking to streamline communication and operational efficiency.

Unveiling the Power of Speech-to-Text API Technology

Speech-to-Text (STT) technology converts spoken language into written text through advanced algorithms, making it an essential tool for various applications like transcription, voice commands, and accessibility. STT systems typically work by analysing audio input, breaking it down into phonetic patterns, and then converting these patterns into meaningful text using linguistic models. Automatic Speech Recognition (ASR) is at the heart of any best speech-to-text API to make this happen. ASR is powered by deep learning and neural networks that continuously improve the system’s accuracy.

Recent advancements in STT API technology have resulted in better accuracy, real-time transcription capabilities, and broader multilingual support. And they continue to evolve. Reinforced privacy measures have also emerged, addressing data security and compliance effectively. 

With the assistance of AI/ML capabilities, advanced speech-to-text models now adapt to different accents, dialects, and specialised vocabularies and even analyse customer sentiments impeccably. These innovations have positioned STT APIs as critical tools for businesses looking to harness the power of voice-driven interactions.

Where can You use Speech to Text APIs? 

Speech-to-text APIs are widely used across various industries. There are special STT APIs built for specific industries too. 

Take banking, where these APIs facilitate secure customer communication and protect confidentiality and trust. 

In call centres, STTs can analyse recorded interactions to identify customer trends and improve service quality.

The medical field leverages STT technology for generating reports, completing forms, streamlining workflows, and ensuring accurate patient identity verification. 

In governance and security, a speech-to-text model assists in the identification and verification process, allowing customers to verbally provide sensitive details like account numbers and dates of birth. 

Consider the media sector, where STT APIs automate the conversion of audio content from TV, radio, and social networks into searchable text, improving content accessibility and management. 

There are more use cases of Speech-to-text APIs, including smart assistants like Siri and Alexa, real-time conversational AI, sales and support enablement, and transcriptions for lectures or live events. Ultimately, the goal of every best speech-to-text API is to improve communication and user experience.

Top 10 Game-Changing Speech-to-Text APIs Present in the Market

Reverie’s Speech-to-Text API

Reverie’s Speech-to-Text API provides a powerful solution for converting voice data into text in 11 Indian languages. With Automatic Speech Recognition (ASR) capabilities, it facilitates real-time transcriptions for customer calls, podcasts, meetings, and more. The API supports effortless integration. It is the best speech-to-text API for businesses to leverage voice commands, keyword spotting, and sentiment analysis. The backbone of all this is Reverie’s flexible technology, which supports various industries, including education, healthcare, and government sectors. 

It ensures high accuracy while maintaining data security through robust encryption. Reverie’s API helps businesses streamline communication, betters customer experience and improves operations with its multilingual, scalable platform.

Pros:

• Transcribes 11 Indian languages accurately.
• Choice of cloud or on-premise deployment.
• Customizable vocabulary models for improved accuracy.
• Real-time and batch processing options for flexible transcription.
• High accuracy, even in noisy environments.
• Developer-friendly with detailed SDKs and documentation.
• Scalable to handle large volumes with low latency.

Cons:

• Expanding language options as of now.
• Customised Models may take time for certain uses.

Google Speech-to-Text

Google Speech-to-Text API is available through the Google Cloud Platform. It integrates with services like Google Drive and Google Docs. Supporting over 50 languages and offering more than 380 voices, it is the best speech-to-text API from Google. DeepMind neural network is used for improved voice quality. Users can choose from various machine learning models depending on the type of audio, such as phone calls or videos. 

The API also includes features for extended punctuation and voice customization. However, audio must be stored in a Google Cloud Storage Bucket for transcription, which may add complexity. 

Pros:

• Provides 60 minutes of free transcription for testing.
• Supports over 125 languages, making it versatile.
• Delivers decent accuracy for many transcription needs.

Cons:

• Requires files to be stored in Google Cloud Bucket, which limits accessibility.
• Setup can be complex for new users.
• Accuracy is lower compared to some competitors.

Deepgram

Deepgram achieves speech-to-text transcription through advanced deep learning models like Base, Enhanced, and the latest Nova-2. Deepgram supports multiple languages, speaker diarization, and smart formatting to upkeep speed and accuracy. 

Flexible deployment options and its ability to handle both real-time and pre-recorded audio from various sources make it the best speech-to-text API. The platform caters to developers with extensive SDK options and dedicated support. They can integrate Deepgram into their voice applications for better use.

Pros:

• Known for its accuracy and speed.
• Features like speaker identification and smart formatting enhance usability.
• Very cost-effective, starting at $0.0043 per minute.

Cons:

• Supports fewer languages than some other providers.
• Primarily targets developers, which may not fit all users.
• New users may face a learning curve.

Amazon Transcribe

Amazon Transcribe provides automatic speech recognition (ASR) to convert speech into text efficiently. While it excels in accuracy for pre-recorded audio, its performance for real-time streaming is less effective. Supporting 31 languages, developers can integrate Amazon Transcriber capabilities into customer service calls and automate subtitling. 

However, users must store audio and video files in S3 buckets. It reflects AWS’s strategy to promote ecosystem integration. Overall, Amazon Transcribe is the best speech-to-text API for AWS users seeking to streamline services within a single platform.

Pros:

• Offers one hour of free transcription each month for the first year.
• Integrates seamlessly with existing AWS services.
• Has specialised options for medical transcription.

Cons:

• Initial setup can be challenging for newcomers.
• Lower accuracy compared to some alternatives.

IBM Watson Speech-to-Text API

IBM Watson is designed to be the best speech-to-text API for enterprise-level transcription. Accordingly, the focus is on customisation and scalability. Supporting seven languages, its performance in accuracy and speed is criticised for high-demand use cases. Customisation options exist albeit requiring significant effort to deploy. While Watson offers on-premises and cloud-based solutions, its overall service is considered more of a legacy option. There is also limited support for modern, fast-paced transcription needs.

Pros:

• Recognized brand in AI and speech technology.
• Part of a broader suite of AI tools.
• Established technology with a long-standing history.

Cons:

• Lower accuracy compared to newer competitors.
• Slower processing times can hinder performance.
• Higher costs with limited customisation options.

Speechmatics

Speechmatics is a cloud-based API focused on high-volume transcription, featuring user-friendly functionality and rapid speeds. It supports 31 languages and primarily serves the UK market as the best speech-to-text API. Speechmatics’ accuracy for pre-recorded audio is considered average. While it offers an affordable entry point, concerns about data retention, speed, and accuracy exist. 

The enhanced version offers better features but at a higher cost. Often, customisation in Speechmatics requires user-provided phonetic inputs. 

Pros:

• Good accuracy for non-English languages and British accents.
• Flexible deployment options, including cloud and on-premises.
• Strong presence in the UK market.

Cons:

• Higher pricing compared to many alternatives.
• Slow transcription speeds for pre-recorded audio.
• Limited support for real-time streaming.

Microsoft Azure

Azure Speech-to-Text (STT) API is recognised for its scalability and support for a wide range of languages. It excels in real-time, custom, and diarisation use cases, making it the best speech-to-text API for challenging transcription scenarios. The features of the custom model allow for tailored training, working on transcription accuracy.

As part of the Azure Cognitive Services suite, Azure STT offers a balanced blend of accuracy and speed. It appeals to enterprises seeking comprehensive AI and ML solutions.

Pros:

• Offers decent accuracy across various languages.
• Integrates well with the Azure ecosystem.
• Supports real-time streaming for immediate responses.

Cons:

• Pricing can be high for small businesses.
• May experience latency issues in real-time transcription.
• Limited options for custom model training.

Assembly AI

AssemblyAI focuses on data analysis and transcription. It brings the best speech-to-text API with features like word confidence scores, multi-speaker recording, and labeling. Assembly AI supports only English. Users can benefit from modern deep-learning models to deliver fast transcription speeds and decent accuracy for both pre-recorded and real-time scenarios. 

The API also includes advanced features such as sentiment analysis, PII redaction, diarisation, language detection, keyword boosting, and higher-level language understanding capabilities.

Pros:

• High accuracy with ongoing enhancements from advanced AI models.
• Supports various audio and video formats for ease of use.
• Provides $50 credits for new users to test the service.

Cons:

• Models are not open-source, limiting customisation.
• Pricing for Speech Understanding varies, complicating cost structure.
• Users may need to familiarise themselves with different model options.

Rev AI

Rev AI is a speech-to-text platform designed for high accuracy in English-language use cases. It is built on over 50,000 hours of transcribed speech. Supporting 36+ languages, Rev AI excels as the best speech-to-text API for English transcription. It may not offer the same level of accuracy for other languages. Rev AI leverages advanced machine learning algorithms and provides features such as language detection, sentiment analysis, and topic detection for enhanced functionality.

Pros:

• Known for accuracy with human-reviewed transcription options.
• Supports both real-time and asynchronous transcription for flexibility.
• Easy integration with various SDKs and APIs for seamless use.

Cons:

• Human-reviewed services can be more expensive.
• Limited free-tier options are available.
• May not serve industries outside of media, legal, and education.

Symbl

Symbl.ai is an AI-driven conversation intelligence platform that provides real-time transcription and analysis across various domains like sales, support, and HR. Utilising natural language processing and machine learning, it extracts insights such as topics, themes, and action items from audio, video, and text conversations. With features tailored for sales and support teams and employee communications, Symbl.ai improves productivity and engagement as the best speech-to-text API for businesses.

Pros:

• Offers advanced contextual understanding, enhancing accuracy.
• Seamless integration with various communication platforms.
• Provides real-time insights for improved engagement.

Cons:

• Integration can be complex and may require technical expertise.
• Some features are exclusive to premium plans.
• May be less useful outside customer service settings.

What makes a Good Speech-to-Text API?

A good Speech-to-Text (STT) API is defined by its flexibility, accuracy, and ease of integration across various platforms. Accurate speech recognition is a fundamental requirement; it ensures precise transcription even in specialized contexts. Reverie’s STT API, for example, achieves this by blending machine learning with linguistic rules, accommodating diverse Indian languages and English, making it suitable for multilingual applications. 

An effective API should also support real-time transcription for instant, seamless processing of live audio—a feature that comes to the rescue in scenarios like voice-activated searches or customer service interactions.

Flexible deployment options are non-negotiable for a Speech-to-Text API’s adaptability. Reverie’s API supports cloud-based and on-premise installations, accommodating specific user needs, privacy concerns, and infrastructure requirements. Comprehensive documentation with code snippets, SDKs, and clear guidelines further smoothens the integration process for developers. Just swift setup and customization are required on your end.

Additional functionalities like profanity filtering, punctuation, formatting, and keyword spotting elevate a Speech-to-Text API’s utility further. Scalability and security? They are imperative for high-traffic scenarios. Moreover, cost-effectiveness and support for multiple audio formats make the API accessible and versatile for various use cases, just like that for Reverie’s STT, from transcription services to speech analytics.

Transform Your Business Operations with Reverie’s Advanced Speech-to-Text API

Reverie’s advanced Speech-to-Text API essentially leverages cutting-edge AI technology to transform business operations through accurate real-time transcription. Supporting 11 Indian languages, it makes communication across diverse linguistic backgrounds effortless, helping your business reach out to a wide range of audiences in real time. 

Reverie’s robust features like precise speech recognition and the ability to analyse sentiment add unmatchable value to your business seeking to provide valuable customer interactions. After all, customers expect brands to do more and you should reciprocate their loyalty in multiple ways. Reverie helps you do that effortlessly.

By integrating Reverie’s STT API, businesses can boost efficiency and reduce turnaround times drastically to serve customers better. What’s more, Reverie’s commitment to data security and support for high volumes of voice inputs means enterprises can confidently embrace voice automation and stay ahead of the competition like never before.

Selecting the right Speech-to-Text API is vital for enhancing communication and operational efficiency. Businesses need reliable, accurate, and feature-rich solutions to stay competitive. Reverie’s Speech-to-Text API offers real-time transcription, multilingual support, and advanced speech recognition, making it a top choice for diverse industries. Explore Reverie’s API today to improve your business’s customer interactions and streamline processes with effortless voice-driven automation.

FAQs

What is the difference between cloud-based and on-premises Speech-to-Text APIs? 

On-premises Speech-to-Text APIs run within a company’s infrastructure, giving it full control and tighter security. In contrast, cloud-based APIs are managed by third-party providers, requiring companies to rely on external management and give up some control over data and security.

What are the common file formats supported by Speech-to-Text APIs for audio input?

Common file formats supported by Speech-to-Text APIs include WAV (Waveform Audio File Format), FLAC (Free Lossless Audio Codec), MP3 (MPEG Audio Layer-3), AAC (Advanced Audio Coding), and M4A (MPEG-4 Audio). Each of these formats varies in parameters like sample rates, compression, bit depth, etc.

Can the Speech-to-Text API accurately recognize and transcribe Indian languages?

Yes, Speech-to-Text APIs are capable of accurately recognising and transcribing Indian languages. For example, Reverie’s STT API supports 11 official Indian languages through precise and real-time conversion of speech into text format.

How do Speech-to-Text APIs ensure the security and privacy of transcribed data?

Speech-to-Text APIs protect data by implementing encryption protocols during transmission and storage. These protocols restrict access to authorized users and often support data anonymization. Many services comply with industry regulations such as GDPR or HIPAA. Such regulations require that sensitive information remains secure and private throughout the transcription process.